Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.
Этот курс входит в специализацию ''Специализация Графические вероятностные модели '
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Специализация Графические вероятностные модели Стэнфордский университетОб этом курсе
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Курс 3 из 3 в программе
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Прибл. 66 часов на выполнение
Английский
Субтитры: Французский, Португальский (Европа), Русский, Английский, Испанский
Приобретаемые навыки
- Algorithms
- Expectation–Maximization (EM) Algorithm
- Graphical Model
- Markov Random Field
Гибкие сроки
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Сертификат, ссылками на который можно делиться с другими людьми
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100% онлайн
Начните сейчас и учитесь по собственному графику.
Курс 3 из 3 в программе
Продвинутый уровень
Прибл. 66 часов на выполнение
Английский
Субтитры: Французский, Португальский (Европа), Русский, Английский, Испанский
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Стэнфордский университет
The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.
Программа курса: что вы изучите
16 минут на завершение
Learning: Overview
This module presents some of the learning tasks for probabilistic graphical models that we will tackle in this course.
16 минут на завершение
1 видео ((всего 16 мин.))
1 ч. на завершение
Review of Machine Learning Concepts from Prof. Andrew Ng's Machine Learning Class (Optional)
This module contains some basic concepts from the general framework of machine learning, taken from Professor Andrew Ng's Stanford class offered on Coursera. Many of these concepts are highly relevant to the problems we'll tackle in this course.
1 ч. на завершение
6 видео ((всего 59 мин.))
2 ч. на завершение
Parameter Estimation in Bayesian Networks
This module discusses the simples and most basic of the learning problems in probabilistic graphical models: that of parameter estimation in a Bayesian network. We discuss maximum likelihood estimation, and the issues with it. We then discuss Bayesian estimation and how it can ameliorate these problems.
2 ч. на завершение
5 видео ((всего 77 мин.))
21 ч. на завершение
Learning Undirected Models
In this module, we discuss the parameter estimation problem for Markov networks - undirected graphical models. This task is considerably more complex, both conceptually and computationally, than parameter estimation for Bayesian networks, due to the issues presented by the global partition function.
21 ч. на завершение
3 видео ((всего 52 мин.))
18 ч. на завершение
Learning BN Structure
This module discusses the problem of learning the structure of Bayesian networks. We first discuss how this problem can be formulated as an optimization problem over a space of graph structures, and what are good ways to score different structures so as to trade off fit to data and model complexity. We then talk about how the optimization problem can be solved: exactly in a few cases, approximately in most others.
18 ч. на завершение
7 видео ((всего 106 мин.))
22 ч. на завершение
Learning BNs with Incomplete Data
In this module, we discuss the problem of learning models in cases where some of the variables in some of the data cases are not fully observed. We discuss why this situation is considerably more complex than the fully observable case. We then present the Expectation Maximization (EM) algorithm, which is used in a wide variety of problems.
22 ч. на завершение
5 видео ((всего 83 мин.))
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Лучшие отзывы о курсе PROBABILISTIC GRAPHICAL MODELS 3: LEARNING
от партнера LC27 авг. 2018 г.
Great course, though with the progress of ML/DL, content seems a touch outdated. Would
от партнера JR28 янв. 2018 г.
Great course! It is pretty difficult - be prepared to study. Leave plenty of time before the final exam.
от партнера LC22 февр. 2019 г.
A great course! Learned a lot. Especially the assignments are excellent! Thanks a lot.
от партнера SP11 окт. 2020 г.
An amazing course! The assignments and quizzes can be insanely difficult espceially towards the conclusion.. Requires textbook reading and relistening to lectures to gather the nuances.
Специализация Графические вероятностные модели : общие сведения
Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.
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Learning Outcomes: By the end of this course, you will be able to
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